Severity Scores in COVID-19 Pneumonia: a Multicenter, Retrospective, Cohort Study

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

BACKGROUND

There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking.

METHODS

A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed.

RESULTS

A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis.

CONCLUSION

Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Utility of ED triage tools in predicting the need for intensive respiratory or vasopressor support in adult patients with COVID-19

BACKGROUND

Serum and radiological parameters used to predict prognosis in COVID patients are not feasible in the Emergency Department. Due to its damaging effect on multiple organs and lungs, scores used to assess multiorgan damage and pneumonia such as Pandemic Medical Early Warning Score (PMEWS), National Early Warning Score 2 (NEWS2), WHO score, quick Sequential Organ Failure Assessment (qSOFA), and DS-CRB 65 can be used to triage patients in the Emergency Department. They can be used to predict patients with the highest risk of seven-day mortality and need for intensive respiratory or vasopressor support (IRVS).

PURPOSE

The primary purpose was to find the score with the highest AUC in predicting IRVS and mortality at seven days. Additional objective was to find out any independent factors associated with IRVS and mortality.

METHODS

The data of adult patients who presented to the Emergency Department (ED) between April 1, 2021 and June 30, 2021 were collected. The WHO score, CRB-65, DS-CRB 65, PMEWS, NEWS2, and qSOFA score were calculated for all patients. Statistical analysis was done and an ROC curve was calculated for all the tools for mortality and need for IRVS at seven days.

FINDINGS

677 patients presented to the Emergency Department with COVID-19 during the period above. Presence of Diabetes Mellitus (p = 0.001), Hypertension (p = 0.001), and chronic kidney disease(CKD) (p = 0.04) was significantly associated with need for IRVS. Age, duration of symptoms, pulse rate, respiratory rate, room air saturation, mental status at admission, and time to IRVS need were identified as independent predictors of in-hospital mortality. The longer the time to IRVS need from ED arrival, the higher the likelihood of mortality. PMEWS (0.830) had the highest AUC, followed by NEWS2 (0.805). A PMEWS cut-off of 6.5 was 74.2% sensitive and 78.3% specific in predicting the need for IRVS. ROC analysis to predict 7-day mortality showed that PMEWS had an AUC of 0.802 (0.766-0.839). QSOFA performed poorly in predicting IRVS (AUC 0.645) and 7-day mortality (AUC 0.677).

CONCLUSION

PMEWS may be used for triaging patients presenting to the Emergency Department with COVID-19 and accurately predicts the need for IRVS and seven day mortality.

Impact of vitamin D supplementation on the clinical outcomes of COVID-19 pneumonia patients: a single-center randomized controlled trial

BACKGROUND

Vitamin D supplementation for infectious diseases has been discussed, but its role in COVID-19 is unclear. Therefore, this study examined the clinical outcomes of COVID-19 pneumonia patients who received vitamin D supplementation.

METHODS

This prospective, open-label, randomized controlled trial was conducted in a university hospital between July 2020 and March 2022. The inclusion criteria were patients aged ≥ 18 years with COVID-19 pneumonia patients. The patients were randomized into two groups: an intervention group receiving vitamin D supplementation (alfacalcidol, two mcg orally daily) until discharge and a control group. The clinical outcomes were pneumonia treatment duration, length of hospital stay, and change in pneumonia severity index between enrollment and discharge. Subgroup analysis was conducted for supplemental oxygen use, high-dose corticosteroid administration, evidence of lymphopenia, C-reactive protein concentration, and total serum vitamin D concentration. Adverse events were monitored.

RESULTS

Two hundred ninety-four patients were recruited (147 per group). The two groups did not differ in pneumonia treatment duration to discharge (p = 0.788) or length of hospital stay (p = 0.614). The reduction in the pneumonia severity index between enrollment and discharge was more significant in the intervention group (p = 0.007); a significant decrease was also observed among patients who had C-reactive protein > 30 mg/L (p < 0.001). No adverse reactions were recorded.

CONCLUSIONS

Adding active vitamin D to standard treatment may benefit COVID-19 pneumonia patients who require supplemental oxygen or high-dose corticosteroid therapy or who have high C-reactive protein concentrations (> 30 mg/L) upon treatment initiation.

TRIAL REGISTRATION

Thai Clinical Trials Registry TCTR20210906005 (retrospectively registered, 6 September 2021).

Predictive performance of SOFA (Sequential Organ Failure Assessment) and qSOFA (quick Sequential Organ Failure Assessment) for in-hospital mortality in ICU patients with COVID-19 of referral center in the north of Iran a retrospective study

Introduction

Patients diagnosed with Coronavirus disease 2019 exhibit varied clinical outcomes, with a reported mortality rate exceeding 30% in those requiring admission to the ICU. The objective of this study was to assess the predictive capacity of Sequential Organ Failure Assessment (SOFA) and quick Sequential Organ Failure Assessment (qSOFA) scores in determining mortality risk among severe COVID-19 patients.

Method and materials

This retrospective study was performed by analyzing the data of patients with COVID-19 who were hospitalized in the ICUs. Data collection of the parameters required to calculate the SOFA and qSOFA Scores were extracted from patient's medical records. All data analysis was performed using SPSS V.25. Significance level considered as P less than 0.05.

Findings

In this study, 258 patients were included. The results showed that the subjects ranged in age from 21 to 98 years with a mean and SD of 62.7±15.6. Of all patients, 127 (49.2%) were female and the rest were male. The mortality rate was 102 (39.5%). The underlying disease of diabetes mellitus with an odds ratio of 1.81 (CI=1.02-3.22) had a significant effect on mortality. In addition, a significant correlation was obtained between admission duration and SOFA score (r=0.147, P=0.018). The SOFA had a very high accuracy of 0.941 and at the cut-off point less than 5 had a sensitivity and specificity of 91.2% and 82.7%. In addition, qSOFA had high accuracy (0.914) and a sensitivity and specificity of 87.3% and 91.7% at the optimal cutting point of greater than 1.

Conclusion

The findings of present study illustrated that deceased COVID-19 patients admitted to the ICU had higher scores on both SOFA and qSOFA scales than surviving patients. Also, both scales have high sensitivity and specificity for anticipating of mortality in these patients. The underlying diabetes mellitus was associated with an increase in patient mortality.

Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

Serum Total Antioxidant Capacity (TAC) and TAC/Lymphocyte Ratio as Promising Predictive Markers in COVID-19

SARS-CoV-2 infection might cause a critical disease, and patients' follow-up is based on multiple parameters. Oxidative stress is one of the key factors in the pathogenesis of COVID-19 suggesting that its level could be a prognostic marker. Therefore, we elucidated the predictive value of the serum non-enzymatic total antioxidant capacity (TAC) and that of the newly introduced TAC/lymphocyte ratio in COVID-19. We included 61 COVID-19 (n = 27 ward, n = 34 intensive care unit, ICU) patients and 29 controls in our study. Serum TAC on admission was measured by an enhanced chemiluminescence (ECL) microplate assay previously validated by our research group. TAC levels were higher (p < 0.01) in ICU (median: 407.88 µmol/L) than in ward patients (315.44 µmol/L) and controls (296.60 µmol/L). Besides the classical parameters, both the TAC/lymphocyte ratio and TAC had significant predictive values regarding the severity (AUC-ROC for the TAC/lymphocyte ratio: 0.811; for TAC: 0.728) and acute kidney injury (AUC-ROC for the TAC/lymphocyte ratio: 0.747; for TAC: 0.733) in COVID-19. Moreover, the TAC/lymphocyte ratio had significant predictive value regarding mortality (AUC-ROC: 0.752). Serum TAC and the TAC/lymphocyte ratio might offer valuable information regarding the severity of COVID-19. TAC measured by our ECL microplate assay serves as a promising marker for the prediction of systemic inflammatory diseases.

Validation of the Barthel Index as a Predictor of In-Hospital Mortality among COVID-19 Patients

In order to predict the high mortality due to COVID-19, simple, useful and remote instruments are required. To assess the validity of the baseline Barthel Index score as a predictor of in-hospital mortality among COVID-19 patients, a validation study of a clinical prediction tool in a cohort of patients with COVID-19 was conducted. The primary variable was mortality and the Barthel Index was the main explanatory variable. Demographic, clinical and laboratory variables were collected. Other mortality predictor scores were also assessed: Pneumonia Severity Index, CURB-65 and A-DROP. The Receiver Operating Characteristic Area under the Curve (ROC AUC), sensitivity and specificity were calculated for both the Barthel Index and the other predictor scores. An analysis of the association between the main variables was conducted, adjusting by means of three multivariate models. Three hundred and twelve patients were studied. Mortality was 16.4%. A mortality Odds Ratio (OR) of 5.95 was associated with patients with a Barthel Index ≤ 90. The model number 3 was developed to predict in-hospital mortality before COVID-19 infection occurs. It exhibits an OR of 3.44, a ROC AUC of 0.792, a sensitivity of 74.5% and a specificity of 73.9%. The Baseline Barthel Index proved useful in our population as a predictor of in-hospital mortality due to COVID-19.

Validation of the qSOFA and CRB-65 in SARS-CoV-2-infected community-acquired pneumonia

Rationale

Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) and CRB-65 (confusion, respiratory rate, blood pressure and age (≥65 years)) risk scores have not been widely evaluated in patients with SARS-CoV-2-positive compared to SARS-CoV-2-negative community-acquired pneumonia (CAP). The aim of the present study was to validate the qSOFA(-65) and CRB-65 scores in a large cohort of SARS-CoV-2-positive and SARS-CoV-2-negative CAP patients.

Methods

We included all cases with CAP hospitalised in 2020 from the German nationwide mandatory quality assurance programme and compared cases with SARS-CoV-2 infection to cases without. We excluded cases with unclear SARS-CoV-2 infection state, transferred to another hospital or on mechanical ventilation during admission. Predefined outcomes were hospital mortality and need for mechanical ventilation.

Results

Among 68 594 SARS-CoV-2-positive patients, hospital mortality (22.7%) and mechanical ventilation (14.9%) were significantly higher when compared to 167 880 SARS-CoV-2-negative patients (15.7% and 9.2%, respectively). All CRB-65 and qSOFA criteria were associated with both outcomes, and age dominated mortality prediction in SARS-CoV-2 (risk ratio >9). Scores including the age criterion had higher area under the curve (AUCs) for mortality in SARS-CoV-2-positive patients (e.g. CRB-65 AUC 0.76) compared to SARS-CoV-2 negative patients (AUC 0.68), and negative predictive value was highest for qSOFA-65=0 (98.2%). Sensitivity for mechanical ventilation prediction was poor with all scores (AUCs 0.59-0.62), and negative predictive values were insufficient (qSOFA-65=0 missed 1490 out of 10 198 patients (∼15%) with mechanical ventilation). Results were similar when excluding frail and palliative patients.

Conclusions

Hospital mortality and mechanical ventilation rates were higher in SARS-CoV-2-positive than SARS-CoV-2-negative CAP. For SARS-CoV-2-positive CAP, the CRB-65 and qSOFA-65 scores showed adequate prediction of mortality but not of mechanical ventilation.

Validation of the COVID-19-12O score for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department

OBJECTIVE

The COVID-19-12O-score has been validated to determine the risk of respiratory failure in patients hospitalized for COVID-19. Our study aims to assess whether the score is effective in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED) to predict readmission and revisit.

METHOD

Retrospective cohort of patients with SARS-CoV-2 pneumonia discharged consecutively from an HUS of a tertiary hospital, from January 7 to February 17, 2021, where we applied the COVID-19-12O -score, with a cut-off point of 9 points to define the risk of admission or revisit. The primary outcome variable was revisit with or without hospital readmission after 30 days of discharge from HUS.

RESULTS

We included 77 patients, with a median age of 59 years, 63.6% men and Charlson index of 2. 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for emergency journal was 0.46 (0.04-4.62, 95% CI, p=0.452), and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O -score is effective in determining the risk of hospital readmission in patients discharged from HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

Clinical and Laboratory Predictors of Mortality in Severe COVID-19 Pneumonia: A Retrospective Study from India

OBJECTIVE

Wide arrays of laboratory parameters have been proposed by many studies for prognosis in COVID-19 patients. In this study, we wanted to determine if the International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score in addition to certain clinical and laboratory parameters would help in predicting mortality. We wanted to determine if a greater severity score on chest x-ray at presentation translated to poor patient outcomes using the COVID-19 chest radiography score.

MATERIAL AND METHODS

This retrospective study was conducted at SDS TRC and Rajiv Gandhi Institute of chest diseases, Bangalore from March 2021 to June 2021. This study included 202 real-time-polymerase chain reaction-positive COVID-19 patients aged above 18 years admitted to the intensive care unit of our hospital. Demographic characteristics and baseline hematological and inflammatory markers (serum C-reactive protein, lactate dehydrogenase, troponin-I, ferritin, and d-dimer) were collected. Radiological severity on a chest x-ray was assessed using the validated COVID-19 chest radiography score. The International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was assigned to each patient within 24 hours of intensive care unit admission. Outcome studied was in-hospital mortality.

RESULTS

The overall mortality was 54.9% (111 cases). Age more than 50 years, >4 days of symptoms, peripheral oxygen saturation/ fraction of inspired oxygen ratio less than 200, elevated serum lactate dehydrogenase >398.5 IU/L, and hypoalbuminemia (<2.95 g/dL) were detected as independent predictors of mortality. A significant correlation of risk stratification with mortality (P = .057) was seen with International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score. There was no significant correlation between the COVID-19 chest radiography score and mortality.

CONCLUSION

Age >50 years, peripheral oxygen saturation/fraction of inspired oxygen ratio <200, mean symptom duration of >4 days, elevated serum lactate dehydrogenase, and hypoalbuminemia are independent predictors of mortality in severe COVID-19 pneumonia. International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was different in the survivors and deceased.

[Validation of the COVID-19-12O scale for predicting readmissions/revisits in patients with SARS-CoV-2 pneumonia discharged from the emergency department]

OBJECTIVE

The COVID-19-12O scale has been validated for determining the risk of respiratory failure in patients hospitalized due to COVID-19. This study aims to assess whether the scale is effective for predicting readmissions and revisits in patients with SARS-CoV-2 pneumonia discharged from a hospital emergency department (HED).

METHOD

This work is a retrospective cohort of consecutive patients with SARS-CoV-2 pneumonia discharged from the HED of a tertiary hospital from January 7 to February 17, 2021. The COVID-19-12O scale with a cut-off point of nine points was used to define the risk of admissions or revisits. The primary outcome variable was a revisit with or without hospital readmission after 30 days of discharge from the HED.

RESULTS

Seventy-seven patients were included. The median age was 59 years, 63.6% were men, and the Charlson Comorbidity Index was 2. A total of 9.1% had an emergency room revisit and 15.3% had a deferred hospital admission. The relative risk (RR) for an HED revisit was 0.46 (0.04-4.62, 95% CI p=0.452) and the RR for hospital readmission was 6.88 (1.20-39.49, 95% CI, p<0.005).

CONCLUSIONS

The COVID-19-12O scale is effective in determining the risk of hospital readmission in patients discharged from an HED with SARS-CoV-2 pneumonia, but is not useful for assessing the risk of revisit.

Comparison Between Physiological Scores SIPF, CURB-65, and APACHE II as Predictors of Prognosis and Mortality in Hospitalized Patients with COVID-19 Pneumonia: A Multicenter Study, Saudi Arabia

Background

A coronavirus pandemic (COVID-19) is associated with catastrophic effects on the world with high morbidity and mortality. We aimed to evaluate the accuracy of physiological shock index (SIPF) (shock index and hypoxemia), CURB -65, acute physiology, and chronic health assessment II (APACHE II) as predictors of prognosis and in-hospital mortality in patients with COVID-19 pneumonia.

Methods

In Saudi Arabia, a multicenter retrospective study was conducted on hospitalized adult patients confirmed to have COVID-19 pneumonia. Information needed to calculate SIPF, CURB-65, and APACHE II scores were obtained from medical records within 24 hours of admission.

Results

The study included 1131 COVID-19 patients who met the inclusion criteria. They were divided into two groups: (A) the ICU group (n=340; 30.1%) and (B) the ward group (n=791; 69.9%). The most common concomitant diseases of patients at initial ICU admission were hypertension (71.5%) and diabetes (62.4%), and most of them were men (63.8%). The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001). The SIPF score showed a significantly higher ability to predict both ICU admission and mortality in patients with COVID-19 pneumonia compared with APACHE II and CURB -65; (AUC 0.89 vs 0.87; p < 0.001) and (AUC 0.89 vs 0.84; p < 0.001) for ICU admission and (AUC 0.90 vs 0.65; p < 0.001) and (AUC 0.90 vs 0.80; p < 0.001) for mortality, respectively.

Conclusion

The ability of the SIPF score to predict ICU admission and mortality in COVID-19 pneumonia is higher than that of APACHE II and CURB-65. The overall mortality was 18.7%, and the mortality rate was higher in the ICU group than in the ward group (39.4% vs 9.6%; p < 0.001).

A practical scoring model to predict the occurrence of critical illness in hospitalized patients with SARS-CoV-2 omicron infection

Background

The variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged repeatedly, especially the Omicron strain which is extremely infectious, so early identification of patients who may develop critical illness will aid in delivering proper treatment and optimizing use of resources. We aimed to develop and validate a practical scoring model at hospital admission for predicting which patients with Omicron infection will develop critical illness.

Methods

A total of 2,459 patients with Omicron infection were enrolled in this retrospective study. Univariate and multivariate logistic regression analysis were performed to evaluate predictors associated with critical illness. Moreover, the area under the receiver operating characteristic curve (AUROC), continuous net reclassification improvement, and integrated discrimination index were assessed.

Results

The derivation cohort included 1721 patients and the validation cohort included 738 patients. A total of 98 patients developed critical illness. Thirteen variables were independent predictive factors and were included in the risk score: age > 65, C-reactive protein > 10 mg/L, lactate dehydrogenase > 250 U/L, lymphocyte < 0.8*10^⁹/L, white blood cell > 10*10^⁹/L, Oxygen saturation < 90%, malignancy, chronic kidney disease, chronic cardiac disease, chronic obstructive pulmonary disease, diabetes, cerebrovascular disease, and non-vaccination. AUROC in the derivation cohort and validation cohort were 0.926 (95% CI, 0.903-0.948) and 0.907 (95% CI, 0.860-0.955), respectively. Moreover, the critical illness risk scoring model had the highest AUROC compared with CURB-65, sequential organ failure assessment (SOFA) and 4C mortality scores, and always obtained more net benefit.

Conclusion

The risk scoring model based on the characteristics of patients at the time of admission to the hospital may help medical practitioners to identify critically ill patients and take prompt measures.

Comparison of CURB-65 Pneumonia Severity Score, Quick COVID-19 Severity Index, and Brescia-COVID Respiratory Severity Scale in Emergently Hospitalized COVID-19 Patients with Pneumonia

Objective

This study aimed to assess the performance of the CURB-65, the quick COVID-19 severity index (qCSI), and the Brescia-COVID respiratory severity scale (BCRSS) scores in predicting ICU (intensive care unit) hospitalization and in-hospital mortality in emergently hospitalized patients with COVID-19 pneumonia.

Materials and Methods

We retrospectively reviewed the emergently hospitalized 258 patients with COVID-19 pneumonia consecutively. The required sample size was calculated to compare the areas under the two ROC (receiver operating characteristic) curves (AUC) using the MedCalc 20.0 program (MedCalc Software Ltd., Ostend, Belgium). In addition, we actualized ROC analyses of the CURB-65, the qCSI, and the BCRSS scores and compared the ROC curves of these three scores.

Results

The median age of the patients was 73, and 63.6% (n=164) were male. Of 258 patients, 29.5% (n=76) were hospitalized in the intensive care unit (ICU), and 15.9% (n=41) died. The CURB-65 and the qCSI scores predicted ICU admission at a moderate level (p≤0.001; AUC values were 0.743 and 0.723, respectively). However, the predictive effect of the BCRSS score for ICU admission was lower (p≤0.001; AUC value was 0.667). The CURB-65 predicted in-hospital mortality at a moderate level ( p≤0.001; AUC value was 0.762). However, the predictive effect of the qCSI and the BCRSS scores for in-hospital mortality were lower ( p≤0.001 and p=0.012, respectively; AUC values were 0.655 and 0.612, respectively).

Conclusion

The CURB-65 score predicted ICU hospitalization and in-hospital mortality better than the qCSI and the BCRSS scores. Also, the qCSI score predicted ICU admission better than the BCRSS score.The predictive effect of the BCRSS score was the lowest. We recommend future studies to evaluate the value and utility of COVID-19 risk classification models.

Effect of asthma, COPD, and ACO on COVID-19: A systematic review and meta-analysis

Introduction

The prevalence of asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO) in patients with COVID-19 varies, as well as their risks of mortality. The present study aimed to assess the prevalence of asthma, COPD, and ACO as comorbidities, and to determine their risks of mortality in patients with COVID-19 using a systematic review and meta-analysis.

Methods

We systematically reviewed clinical studies that reported the comorbidities of asthma, COPD, and ACO in patients with COVID-19. We searched various databases including PubMed (from inception to 27 September 2021) for eligible studies written in English. A meta-analysis was performed using the random-effect model for measuring the prevalence of asthma, COPD, and ACO as comorbidities, and the mortality risk of asthma, COPD, and ACO in patients with COVID-19 was estimated. A stratified analysis was conducted according to country.

Results

One hundred one studies were eligible, and 1,229,434 patients with COVID-19 were identified. Among them, the estimated prevalence of asthma, COPD, and ACO using a meta-analysis was 10.04% (95% confidence interval [CI], 8.79–11.30), 8.18% (95% CI, 7.01–9.35), and 3.70% (95% CI, 2.40–5.00), respectively. The odds ratio for mortality of pre-existing asthma in COVID-19 patients was 0.89 (95% CI, 0.55–1.4; p = 0.630), while that in pre-existing COPD in COVID-19 patients was 3.79 (95% CI, 2.74–5.24; p<0.001). France showed the highest prevalence of asthma followed by the UK, while that of COPD was highest in the Netherlands followed by India.

Conclusion

Pre-existing asthma and COPD are associated with the incidence of COVID-19. Having COPD significantly increases the risk of mortality in patients with COVID-19. These differences appear to be influenced by the difference of locations of disease pathophysiology and by the daily diagnosis and treatment policy of each country.

Hypopharynx, oropharynx morphology and histology in severe Coronavirus 2 patients treated by noninvasive ventilation: comparison between full-face mask and helmet strategies

BACKGROUND

Non-invasive ventilation may alter the morphology and histology of the upper airway mucosa. This study aimed to investigate the alterations of hypopharynx and oropharynx mucosa, identified during oro-tracheal intubation procedure via video-assisted laryngoscopy, in severe acute respiratory syndrome Coronavirus 2 related, treated by non-invasive ventilation via full-face mask or helmet.

METHODS

Data of patients affected by Coronavirus 2 admitted to COVID Hospital of L'Aquila (Italy), presenting hypopharynx and oropharynx morphology alterations, requiring oro-tracheal intubation for invasive ventilation and initially treated with non-invasive ventilation were included in the study. The study aimed to investigate the upper airway mucosa alterations using oropharyngeal and hypopharyngeal images and biopsies taken during video-assisted-laryngoscopy. Data from the hypopharynx and oropharynx morphology and histology alterations between non-invasive ventilation via a full-face mask or helmet used during hospitalization were compared.

RESULTS

From 220 data recorded, 60 patients were included in the study and classified into non-invasive ventilation full-face mask group (30/60) and via helmet group. Comparing data between groups, significant differences were found with respect to hyperemia (77% vs. 20%), laryngeal bleeding ulcerations (87% vs. 13%), and vocal cord edema with >50% narrowing of the tracheal lumen (73% vs. 7%), respectively. The histology examination revealed fibrin-necrotic exudate with extensive necrotic degenerative changes in the sample tissue of the groups. There were not any differences in the duration time of non-invasive ventilation, time from hospitalization and the start of ventilation between groups.

CONCLUSIONS

The data from this research suggested that there were differences in airway mucosa damages among patients treated with a full-face mask or helmet. Further studies should be planned to understand which non-invasive ventilation support may mitigate upper airway mucosa damages when oro-tracheal intubation is requested for invasive respiratory support.

Physiologic Scoring Systems in Predicting the COVID-19 Patients' one-month Mortality; a Prognostic Accuracy Study

Introduction : It is critical to quickly and easily identify severe coronavirus disease 2019 (COVID-19) patients and predict their mortality. This study aimed to determine the accuracy of the physiologic scoring systems in predicting the mortality of COVID-19 patients. Methods: This prospective cross-sectional study was performed on COVID-19 patients admitted to the emergency department (ED). The clinical characteristics of the participants were collected by the emergency physicians and the accuracy of the Quick Sequential Failure Assessment (qSOFA), Coronavirus Clinical Characterization Consortium (4C) Mortality, National Early Warning Score-2 (NEWS2), and Pandemic Respiratory Infection Emergency System Triage (PRIEST) scores for mortality prediction was evaluated. Results: Nine hundred and twenty-one subjects were included. Of whom, 745 (80.9%) patients survived after 30 days of admission. The mean age of patients was 59.13 ± 17.52 years, and 550 (61.6%) subjects were male. Non-Survived patients were significantly older (66.02 ± 17.80 vs. 57.45 ± 17.07, P< 0.001) and had more comorbidities (diabetes mellitus, respiratory, cardiovascular, and cerebrovascular disease) in comparison with survived patients. For COVID-19 mortality prediction, the AUROCs of PRIEST, qSOFA, NEWS2, and 4C Mortality score were 0.846 (95% CI [0.821-0.868]), 0.788 (95% CI [0.760-0.814]), 0.843 (95% CI [0.818-0.866]), and 0.804 (95% CI [0.776-0.829]), respectively. All scores were good predictors of COVID-19 mortality. Conclusion: All studied physiologic scores were good predictors of COVID-19 mortality and could be a useful screening tool for identifying high-risk patients. The NEWS2 and PRIEST scores predicted mortality in COVID-19 patients significantly better than qSOFA.

Prognostication of COVID-19 patients using ROX index and CURB-65 score - A retrospective observational study

Objectives

Coronavirus disease-2019 (COVID-19) disease has overwhelmed the healthcare infrastructure worldwide. The shortage of intensive care unit (ICU) beds leads to longer waiting times and higher mortality for patients. High crowding leads to an increase in mortality, length of hospital stays, and hospital costs for patients. Through an appropriate stratification of patients, rational allocation of the available hospital resources can be accomplished. Various scores for risk stratification of patients have been tried, but for a score to be useful at primary care level, it should be readily available at the bedside and be reproducible. ROX index and CURB-65 are simple bedside scores, requiring minimum equipment, and investigations to calculate.

Methods

This retrospective, record-based study included adult patients who presented to the ED from May 1, 2020 to November 30, 2020 with confirmed COVID-19 infection. The patient's clinical and demographic details were obtained from the electronic medical records of the hospital. ROX index and CURB-65 score on ED arrival were calculated and correlated with the need for hospitalization and early (14-day) and late (28-day) mortality.

Results

842 patients were included in the study. The proportion of patients with mild, moderate and severe disease was 46.3%, 14.9%, and 38.8%, respectively. 55% patients required hospitalization. The 14-day mortality was 8.8% and the 28-day mortality was 20.7%. The AUROC of ROX index for predicting hospitalization was 0.924 (p < 0.001), for 14-day mortality was 0.909 (p < 0.001) and for 28-day mortality was 0.933 (p < 0.001). The AUROC of CURB-65 score for predicting hospitalization was 0.845 (p < 0.001), for 14-day mortality was 0.905 (p < 0.001) and for 28-day mortality was 0.902 (p < 0.001). The cut-off of ROX index for predicting hospitalization was ≤18.634 and for 14-day mortality was ≤14.122. Similar cut-off values for the CURB-65 score were ≥1 and ≥2, respectively.

Conclusion

ROX index and CURB-65 scores are simple and inexpensive scores that can be efficiently utilised by primary care physicians for appropriate risk stratification of patients with COVID-19 infection.

MuLBSTA skorunun SARS-CoV-2 pnömonili hospitalize hastalarda kritik klinik sonuçları öngörmedeki prediktif değerinin incelenmesi

Giriş:MuLBSTA (Multilobar infiltrasyon, Lenfositopeni, Bakteriyel koenfeksiyon, Sigara öyküsü, hiperTansiyon ve Yaş> 65) skoru, viral pnömonisi olan hastaları beklenen mortaliteye göre sınıflandırmak için kullanılan bir klinik tahmin kuralıdır. Hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçlar için MuLBSTA'nın prediktif performansını PSI, CURB-65 ve qSOFA ile karşılaştırdık. Metot:Bu çalışma 11 Mart 2020 ile 31 Mayıs 2020 tarihleri arasında üçüncü basamak bir üniversite hastanesinde yatan Sars-Cov-2'li hastalar üzerinde geriye dönük yapıldı. SARS-Cov-2 testi pozitif çıkan 900 hastadan 271'i çalışmaya dahil edildi. Tüm hastalarda 30 günlük mortalite, YBÜ ihtiyacı, mekanik ventilasyon gereksinimi ve ARDS gelişimini değerlendirmek için MuLBSTA, PSI, CURB65 ve qSOFA skoru kullanıldı. 30 günlük mortalite için prognostik faktörler de analiz edildi. Bulgular:Hastanede yatan 271 hastanın 150'si (%55.3) erkekti. Ortalama yaş 54.2 ± 15.4 yıldı. 30 günlük ölüm oranı %10,7 idi. Çalışmaya dahil edilen hastalardan; 39 hasta (%14,3) yoğun bakıma yatırıldı, 32 hasta (%11,8) mekanik ventilatör desteği aldı ve 23 hasta (%8,4) ARDS tanısı aldı. Mortaliteyi tahmin etmede MuLBSTA, PSI, CURB-65 ve qSOFA skorlarının alıcı işletim karakteristik eğrisi altında kalan alan(AUROC) değerleri sırasıyla 0.877 (%95 CI 0.832 0.914), 0.853 (%95 CI 0.806-0.893), 0.769 (95% CI 0,714-0,817) ve 0,769 (95% CI 0,715-0,818). MuLBSTA puanı, diğer tahmin puanlarına kıyasla daha yüksek bir AUROC değeri gösterdi. MuLBSTA ve PSI skorları, YBÜ ihtiyacı, mekanik ventilasyon gereksinimive ARDS gelişimi olan hastaları belirlemede CURB-65 ve qSOFA skorlarından daha iyi performans gösterdi. Sonuç:MuLBSTA skoru, hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçları tahmin etmek için etkili bir araçtır. Kullanımını doğrulamak için daha fazla çalışmaya ihtiyaç vardır.

The Low Expression of Fc-Gamma Receptor III (CD16) and High Expression of Fc-Gamma Receptor I (CD64) on Neutrophil Granulocytes Mark Severe COVID-19 Pneumonia

Hyperinflammation through neutrophil granulocytes contributes to disease severity in COVID-19 pneumonia and promotes acute lung failure. Understanding the mechanisms of the dysregulations within the myeloid cell compartment may help to improve therapies for severe COVID-19 infection. Here, we investigated the immunopathological characteristics of circulating neutrophil granulocytes and monocytes in 16 patients with COVID-19 pneumonia by multiparameter flow cytometry in comparison to 9 patients with pulmonary infiltrates but without COVID-19. We correlated the immunophenotypes with the scores of the severity-of-disease classification system, APACHE-II. We found that the mean fluorescence intensity (MFI) of CD15, which is important for the transendothelial migration, was significantly reduced in the patients with COVID-19 (difference ± SD; 295.70 ± 117.50 MFI; p = 0.02). In addition, the granularity was significantly lower in the neutrophil granulocytes of patients with COVID-19 (difference ± SD; 1.11 ± 0.43 side-scatter ratio; p = 0.02). Moreover, the Fc-gamma receptor III (CD16) and Fc-gamma receptor I (CD64) on the neutrophil granulocytes were expressed discordantly with COVID-19 severity. CD16 correlated as inversely proportional (ρ = (−)0.72; 95% CI (−)0.92–(−)0.23; p = 0.01) and CD64 as proportional (ρ = 0.76; 95% CI 0.31–0.93; p = 0.01) with the APACHE-II scores of the patients. We conclude that the deviant expression of the Fc-gamma receptors might play role in a dysregulated antibody-mediated phagocytosis in severe cases of COVID-19 pneumonia.

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study

Background

The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection.

Methods and analysis

Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses.

Results

A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62).

Conclusion

Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.

External validation of risk scores to predict in-hospital mortality in patients hospitalized due to coronavirus disease 2019

BACKGROUND

The coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Prediction models that accurately estimate mortality risk in hospitalized patients could assist medical staff in treatment and allocating limited resources.

AIMS

To externally validate two promising previously published risk scores that predict in-hospital mortality among hospitalized COVID-19 patients.

METHODS

Two prospective cohorts were available; a cohort of 1028 patients admitted to one of nine hospitals in Lombardy, Italy (the Lombardy cohort) and a cohort of 432 patients admitted to a hospital in Leiden, the Netherlands (the Leiden cohort). The endpoint was in-hospital mortality. All patients were adult and tested COVID-19 PCR-positive. Model discrimination and calibration were assessed.

RESULTS

The C-statistic of the 4C mortality score was good in the Lombardy cohort (0.85, 95CI: 0.82-0.89) and in the Leiden cohort (0.87, 95CI: 0.80-0.94). Model calibration was acceptable in the Lombardy cohort but poor in the Leiden cohort due to the model systematically overpredicting the mortality risk for all patients. The C-statistic of the CURB-65 score was good in the Lombardy cohort (0.80, 95CI: 0.75-0.85) and in the Leiden cohort (0.82, 95CI: 0.76-0.88). The mortality rate in the CURB-65 development cohort was much lower than the mortality rate in the Lombardy cohort. A similar but less pronounced trend was found for patients in the Leiden cohort.

CONCLUSION

Although performances did not differ greatly, the 4C mortality score showed the best performance. However, because of quickly changing circumstances, model recalibration may be necessary before using the 4C mortality score.

C-Reactive Protein and Serum Albumin Ratio: A Feasible Prognostic Marker in Hospitalized Patients with COVID-19

(1) Background: C-reactive protein (CRP) and albumin are inflammatory markers. We analyzed the prognostic capacity of serum albumin (SA) and CRP for an outcome comprising mortality, length of stay, ICU admission, and non-invasive mechanical ventilation in hospitalized COVID-19 patients. (2) Methods: We conducted a retrospective cohort study based on the Spanish national SEMI-COVID-19 Registry. Two multivariate logistic models were adjusted for SA, CRP, and their combination. Training and testing samples were used to validate the models. (3) Results: The outcome was present in 41.1% of the 3471 participants, who had lower SA (mean [SD], 3.5 [0.6] g/dL vs. 3.8 [0.5] g/dL; p < 0.001) and higher CRP (108.9 [96.5] mg/L vs. 70.6 [70.3] mg/L; p < 0.001). In the adjusted multivariate model, both were associated with poorer evolution: SA, OR 0.674 (95% CI, 0.551−0.826; p < 0.001); CRP, OR 1.002 (95% CI, 1.001−1.004; p = 0.003). The CRP/SA model had a similar predictive capacity (honest AUC, 0.8135 [0.7865−0.8405]), with a continuously increasing risk and cutoff value of 25 showing the highest predictive capacity (OR, 1.470; 95% CI, 1.188−1.819; p < 0.001). (4) Conclusions: SA and CRP are good independent predictors of patients hospitalized with COVID-19. For the CRP/SA ratio value, 25 is the cutoff for poor clinical course.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

A Simple Prognostic Score for Critical COVID-19 Derived from Patients without Comorbidities Performs Well in Unselected Patients

We aimed to search for laboratory predictors of critical COVID-19 in consecutive adults admitted in an academic center between 16 September 2020−20 December 2021. Patients were uniformly treated with low-molecular-weight heparin, and dexamethasone plus remdesivir when SpO2 < 94%. Among consecutive unvaccinated patients without underlying medical conditions (n = 241, 49 year-old median, 71% males), 22 (9.1%) developed critical disease and 2 died (0.8%). White-blood-cell counts, neutrophils, neutrophil-to-lymphocyte ratio, CRP, fibrinogen, ferritin, LDH and γ-GT at admission were each univariably associated with critical disease. ROC-defined cutoffs revealed that CRP > 61.8 mg/L, fibrinogen > 616.5 mg/dL and LDH > 380.5 U/L were each associated with critical disease development, independently of age, sex and days from symptom-onset. A score combining higher-than-cutoff CRP (0/2), LDH (0/1) and fibrinogen (0/1) predicted critical disease (AUC: 0.873, 95% CI: 0.820−0.926). This score performed well in an unselected patient cohort (n = 1228, 100% unvaccinated) predominantly infected by the alpha variant (AUC: 0.718, 95% CI: 0.683−0.753), as well as in a mixed cohort (n = 527, 65% unvaccinated) predominantly infected by the delta variant (AUC: 0.708, 95% CI: 0.656−0.760). Therefore, we propose that a combination of standard biomarkers of acute inflammatory response, cell death and hypercoagulability reflects the severity of COVID-19 per se independently of comorbidities, age and sex, being of value for risk stratification in unselected patients.

The Effectiveness of National Early Warning Score, Quick Sequential Organ Failure Assessment, Charlson Comorbidity Index, and Elixhauser Comorbidity Index Scores in Predicting Mortality Due to COVID-19 in Elderly Patients

Introduction: As the mortality rate in coronavirus disease 2019 (COVID-19) patients older than 65 years is considerable, evaluation of in-hospital mortality is crucial. This study aimed to evaluate in-hospital mortality in COVID-19 patients older than 65 years using the National Early Warning Score (NEWS), Quick Sequential Organ Failure Assessment (q-SOFA), Charlson Comorbidity Index (CCI), and Elixhauser Comorbidity Index (ECI).

Methods: This retrospective study included data from 480 patients with confirmed COVID-19 and age over 65 years who were evaluated in a university emergency department in Turkey. Data from eligible but deceased COVID-19 patients was also included. NEWS, q-SOFA, CCI, and ECI scores were retrospectively calculated. All clinical data was accessed from the information management system of the hospital, retrieved, and analyzed.

Results: In-hospital mortality was seen in 169 patients (169/480). Low oxygen saturation, high C-reactive protein (CRP) and urea levels, and high q-SOFA and ECI scores helped us identify mortality in high-risk patients. A statistically significant difference was found in mortality estimation between q-SOFA and ECI (p <0.001), respectively.

Conclusion: Q-SOFA and ECI can be used both easily and practically in the early diagnosis of in-hospital mortality in COVID-19 positive patients over 65 years of age admitted to the emergency department. Low oxygen saturation, high CRP and urea levels, and high q-SOFA and ECI scores are helpful in identifying high-risk patients.

Prediction models for severe manifestations and mortality due to COVID-19: A systematic review

BACKGROUND

Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.

OBJECTIVE

This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.

METHODS

Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.

RESULTS

A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability.

CONCLUSION

Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,¹⁵ and Knight et al.,¹⁸ which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Clinical features and risk factors for mortality among long-term care facility residents hospitalized due to COVID-19 in Spain

BACKGROUND

COVID-19 severely impacted older adults and long-term care facility (LTCF) residents. Our primary aim was to describe differences in clinical and epidemiological variables, in-hospital management, and outcomes between LTCF residents and community-dwelling older adults hospitalized with COVID-19. The secondary aim was to identify risk factors for mortality due to COVID-19 in hospitalized LTCF residents.

METHODS

This is a cross-sectional analysis within a retrospective cohort of hospitalized patients≥75 years with confirmed COVID-19 admitted to 160 Spanish hospitals. Differences between groups and factors associated with mortality among LTCF residents were assessed through comparisons and logistic regression analysis.

RESULTS

Of 6,189 patients≥75 years, 1,185 (19.1%) were LTCF residents and 4,548 (73.5%) were community-dwelling. LTCF residents were older (median: 87.4 vs. 82.1 years), mostly female (61.6% vs. 43.2%), had more severe functional dependence (47.0% vs 7.8%), more comorbidities (Charlson Comorbidity Index: 6 vs 5), had dementia more often (59.1% vs. 14.4%), and had shorter duration of symptoms (median: 3 vs 6 days) than community-dwelling patients (all, p<.001). Mortality risk factors in LTCF residents were severe functional dependence (aOR:1.79;95%CI:1.13-2.83;p=.012), dyspnea (1.66;1.16-2.39;p=.004), SatO2<94% (1.73;1.27-2.37;p=.001), temperature≥37.8ºC (1.62;1.11-2.38; p=.013); qSOFA index≥2 (1.62;1.11-2.38;p=.013), bilateral infiltrates (1.98;1.24-2.98;p<.001), and high C-reactive protein (1.005;1.003-1.007;p<.001). In-hospital mortality was initially higher among LTCF residents (43.3% vs 39.7%), but lower after adjusting for sex, age, functional dependence, and comorbidities (aOR:0.74,95%CI:0.62-0.87;p<.001).

CONCLUSION

Basal functional status and COVID-19 severity are risk factors of mortality in LTCF residents. The lower adjusted mortality rate in LTCF residents may be explained by earlier identification, treatment, and hospitalization for COVID-19.

Performance of the CURB-65, ISARIC-4C and COVID-GRAM scores in terms of severity for COVID-19 patients

BACKGROUND

In the COVID-19 pandemic, difficulties have been experienced in the provision of healthcare services because of excessive patient admissions to hospitals and emergency departments. It has become important to use clear and objective criteria for the early diagnosis of patients with high-risk classification and clinical worsening risk.

OBJECTIVE

The aim of this study was to assess the prognostic accuracy of CURB-65, ISARIC-4C and COVID-GRAM scores in patients hospitalised for COVID-19 and to compare the scoring systems in terms of predicting in-hospital mortality and intensive care unit requirement.

METHODS

The files of all COVID-19 patients over the age of 18 who were admitted to the emergency department and hospitalised between September 1, 2020 and December 1, 2020 were retrospectively scanned. The area under the receiver operating characteristic curve and Youden J Index were used to compare scoring systems for predicting in-hospital mortality and intensive care requirement.

RESULTS

There were 481 patients included in this study. The median age of the patients was 67 (52-79). In terms of in-hospital mortality, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.846, 0.784 and 0.701 respectively. In terms of intensive care requirement, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.898, 0.797 and 0.684 respectively. In our study, Youden's J indexes of CURB-65, ISARIC-4C and COVID-GRAM scores were found to be 0.59, 0.27 and 0.01 respectively, for mortality prediction of COVID-19 patients. Whereas Youden's J indexes were found to be 0.63, 0.26 and 0.01 respectively for determining intensive care requirement.

CONCLUSIONS

Among the scoring systems assessed, CURB-65 score had better performance in predicting in-hospital mortality and ICU requirement in COVID-19 patients. ISARIC-4C has been found successful in identifying low-risk patients and the use of the ISARIC-4C score with CURB-65 increases the accuracy of risk assessment.

Mortality risk factors in a Spanish cohort of oldest-old patients hospitalized with COVID-19 in an acute geriatric unit: the OCTA-COVID study

Aim

The objective of this study is to describe the baseline characteristics of oldest-old patients admitted with COVID-19 to an acute geriatric unit and to determine the factors associated with in-hospital mortality.

Findings

Dementia, incident delirium, and the CURB-65 score ≥ 3 are independent mortality risk factors. The concurrent use of angiotensin-converting enzyme inhibitors is a protective factor.

Message

Recognition of geriatric syndromes may be useful to help clinicians establish the prognosis of oldest-old patients admitted to hospital with COVID-19.

Purpose

To determine predictors of in-hospital mortality related to COVID-19 in oldest-old patients.

Design

Single-center observational study.

Setting and participants

Patients ≥ 75 years admitted to an Acute Geriatric Unit with COVID-19.

Methods

Data from hospital admission were retrieved from the electronic medical records: demographics, geriatric syndromes (delirium, falls, polypharmacy, functional and cognitive status) co-morbidities, previous treatments, clinical, laboratory, and radiographic characteristics. Cox proportional hazard models were used to evaluate in-hospital mortality.

Results

Three hundred patients were consecutively included (62.7% females, mean age of 86.3 ± 6.6 years). Barthel Index (BI) was < 60 in 127 patients (42.8%) and 126 (42.0%) had Charlson Index CI ≥ 3. Most patients (216; 72.7%) were frail (Clinical Frailty Scale ≥ 5) and 134 patients (45.1%) had dementia of some degree. The overall in-hospital mortality rate was 37%. The following factors were associated with higher in-hospital mortality in a multi-variant analysis: CURB-65 score = 3–5 (HR 7.99, 95% CI 3.55–19.96, p < 0.001), incident delirium (HR 1.72, 1.10–2.70, p = 0.017) and dementia (HR 3.01, 95% CI 1.37–6.705, p = 0.017). Protective factors were concurrent use of angiotensin-converting enzyme inhibitors (HR 0.42, 95% CI 0.25–0.72, p = 0.002) or prescription of hydroxychloroquine (HC 0.37 95% CI 0.22–0.62, p < 0.001) treatment during admission.

Conclusions and implications

Our findings suggest that recognition of geriatric syndromes together with the CURB-65 score may be useful tools to help clinicians establish the prognosis of oldest-old patients admitted to hospital with COVID-19.

Performance of CURB-65, PSI, and APACHE-II for predicting COVID-19 pneumonia severity and mortality

No prognostic tools for the prediction of COVID-19 pneumonia severity and mortality are available. We explored whether CURB-65, PSI, and APACHE-II could predict COVID-19 pneumonia severity and mortality. We included 167 patients with confirmed COVID-19 pneumonia in this retrospective study. The severity and 30-day mortality of COVID-19 pneumonia were predicted using PSI, CURB-65, and APACHE-II scales. Kappa test was performed to compare the consistency of the three scales. There was a significant difference in the distribution of the scores of the three scales ( P < 0.001). Patients with PSI class ⩽III, CURB-65 ⩽1, and APACHE-II-I all survived. The ROC analysis showed the areas under the curve of the PSI, CURB-65, and APACHE-II scales were 0.83 (95% CI, 0.74–0.93), 0.80 (95% CI, 0.69–0.90), and 0.83 (95% CI, 0.75–0.92), respectively. Our findings suggest that PSI and CURB-65 might be useful to predict the severity and mortality of COVID-19 pneumonia.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.